Breast cancer patients using aromatase inhibitors (AIs) as an adjuvant therapy often report side effects, including hot flashes, mood changes, and cognitive impairment. Despite long-term use in humans, little is known about the effects of continuous AI administration on the brain and cognition. We used a primate model of human cognitive aging, the common marmoset, to examine the effects of a 4-week daily administration of the AI letrozole (20 g, p.o.) on cognition, anxiety, thermoregulation, brain estrogen content, and hippocampal pyramidal cell physiology. Letrozole treatment was administered to both male and female marmosets and reduced peripheral levels of estradiol (E2), but unexpectedly increased E2 levels in the hippocampus. Spatial working memory and intrinsic excitability of hippocampal neurons were negatively affected by the treatment possibly due to increased hippocampal E2. While no changes in hypothalamic E2 were observed, thermoregulation was disrupted by letrozole in females only, indicating some impact on hypothalamic activity. These findings suggest adverse effects of AIs on the primate brain and call for new therapies that effectively prevent breast cancer recurrence while minimizing side effects that further compromise quality of life.

With the oncoming age of big data, biologists are encountering more use cases for cloud-based computing to streamline data processing and storage. Unfortunately, cloud platforms are difficult to learn, and there are few resources for biologists to demystify them. We have developed a guide for experimental biologists to set up cloud processing on Amazon Web Services to cheaply outsource data processing and storage. Here we provide a guide for setting up a computing environment in the cloud and showcase examples of using Python and Julia programming languages. We present example calcium imaging data in the zebrafish brain and corresponding analysis using suite2p software. Tools for budget and user management are further discussed in the attached protocol. Using this guide, researchers with limited coding experience can get started with cloud-based computing or move existing coding infrastructure into the cloud environment.

With the oncoming age of big data, biologists are encountering more use cases for cloud-based computing to streamline data processing and storage. Unfortunately, cloud platforms are difficult to learn, and there are few resources geared towards biologists for demystifying them. We have developed a guide for experimental biologists to set up cloud processing on Amazon Web Services to cheaply outsource data processing and storage. Here we provide a guide on setting up a computing environment in the cloud and showcase examples of using Python and Julia programming languages. We present example calcium imaging data in the zebrafish brain and corresponding analysis using suite2p software. Tools for management of users and budgets are discussed in the protocol. Following this guide should help researchers even with limited programming experience to get started or move existing coding infrastructure into the cloud environment.

Citizen Science or community science has been around for a long time. The scope of community involvement in Citizen Science initiatives ranges from short-term data collection to intensive engagement to delve into a research topic together with scientists and/or other volunteers. Although many volunteer researchers have academic training, it is not a prerequisite for participation in research projects. It is important to adhere to scientific standards, which include, above all, transparency with regard to the methodology of data collection and public discussion of the results, and open educational resources (OER). Hereby, Citizen Science is closely linked to Open Science. In our contribution, we will introduce two projects, both developed within the Wikimedia Fellowship Freies Wissen. The top-down approach: ERGo! An Entomology Research Tool to raise awareness of biodiversity protection. Inclusion in academia and pressing social problems such as climate change are fundamentally social justice issues. To facilitate early participation in the scientific process on the part of people holding underrepresented identities in science, we develop a Citizen Science initiative based on a low-cost open-source platform (ERGo!) to perform a technique for electrical recordings from insect eyes known as electroretinograms (ERGs) while presenting visual stimuli. Pasadena Unified School District High School students pilot ERG experiments to test the feasibility of this technique as a large-scale Citizen Science initiative. With ERGo!, future Citizen Scientists contribute data to cutting-edge research that monitors insect biodiversity, adaptation, and health in rapidly changing environments caused by monocultures, pesticides, and climate change. The bottom-up approach: Open cultural data collection. A Citizen Science initiative for regional knowledge curation. We catalogued the 18th century German magazine ‘Die Gartenlaube’ (in Wikisource) with bibliographic metadata in Wikidata in a project called ‘Die Datenlaube’. We develop collaborative approaches for linked open data methods to produce data sets about historical knowledge. The concept of ‘Open Citizen Science’ offers a methodological baseline for Open Science practises in fields of digital humanities. Scanned documents and structured open metadata revealed open access to historic collections. Through the Wikimedia platforms 'Die Datenlaube' creates possibilities to edit entries, to design own investigations, and to contribute to OER. Based on the elaboration of the two rather different projects (natural and social sciences, involvement of pupils vs citizens, top-down vs bottom-up), we will discuss similarities and hence the challenges and lessons learned for using and developing Open Science elements in Citizen Science and mutual learning. Furthermore, we will conclude by focusing on the opportunities resulting from the integration of societal expectations in science and vice versa.